{"id":"W2794223847","doi":"10.1002/asi.24000","title":"A new approach to web co‐link analysis","year":2018,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"Web visibility and informetrics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Link (geometry); Link analysis; Computer science; Data link; Variety (cybernetics); Data science; World Wide Web; Data mining; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003856027,0.001446043,0.001216607,0.01858781,0.001872041,0.006030046,0.002327378,0.001627421,0.004738993],"category_scores_gemma":[0.01863095,0.0007607423,0.001990264,0.01705376,0.00149595,0.005284298,0.004366816,0.003327352,0.002473645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191208,"about_ca_system_score_gemma":0.001995879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004427564,"about_ca_topic_score_gemma":0.004640819,"domain_scores_codex":[0.9923487,0.002126053,0.0005330203,0.001809412,0.002897284,0.0002855143],"domain_scores_gemma":[0.9882284,0.005403569,0.001083585,0.002432987,0.00255812,0.0002931994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002302641,0.0009131473,0.03315602,0.000712344,0.001031801,0.0006451558,0.003574384,0.04169183,0.01308515,0.188895,0.02155476,0.6945102],"study_design_scores_gemma":[0.00004921298,0.0001036636,0.01290887,0.0001071563,0.0002018708,0.0009209118,0.0015015,0.7003822,0.007627165,0.1937208,0.0823205,0.0001562223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005650094,0.0001322888,0.9888758,0.0002375594,0.00007178987,0.0001886702,0.0009889517,0.00144695,0.002407954],"genre_scores_gemma":[0.07121161,0.0001933219,0.9226317,0.0001248497,0.0001632205,0.0005754164,0.002174156,0.0003756452,0.002550063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9814122,"threshold_uncertainty_score":0.02039289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557187086333424,"score_gpt":0.2774964609037368,"score_spread":0.2619245900404026,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}